Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs

Jueqing Lu, Lan Du, Ming Liu, Joanna Dipnall


Abstract
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification. The model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations. Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III ) and the EU legislation dataset show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.
Anthology ID:
2020.emnlp-main.235
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2935–2943
Language:
URL:
https://aclanthology.org/2020.emnlp-main.235
DOI:
10.18653/v1/2020.emnlp-main.235
Bibkey:
Cite (ACL):
Jueqing Lu, Lan Du, Ming Liu, and Joanna Dipnall. 2020. Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2935–2943, Online. Association for Computational Linguistics.
Cite (Informal):
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs (Lu et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.235.pdf
Video:
 https://slideslive.com/38938967
Code
 MemoriesJ/KAMG
Data
EURLEX57KMIMIC-III